Triple

T14388415
Position Surface form Disambiguated ID Type / Status
Subject Google Silicon team E356781 entity
Predicate responsibleFor P636 FINISHED
Object Google Tensor system-on-chip E72121 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Google Tensor system-on-chip | Statement: [Google Silicon team, responsibleFor, Google Tensor system-on-chip]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Google Tensor system-on-chip
Context triple: [Google Silicon team, responsibleFor, Google Tensor system-on-chip]
  • A. Google Tensor chosen
    Google Tensor is Google's custom-designed system-on-a-chip (SoC) platform created to power Pixel devices with advanced AI and machine learning capabilities.
  • B. Tensor Processing Unit
    A Tensor Processing Unit (TPU) is a specialized AI accelerator chip designed by Google to efficiently perform large-scale machine learning computations, particularly for neural networks.
  • C. Google TPU
    Google TPU is a custom-designed application-specific integrated circuit (ASIC) developed by Google to accelerate machine learning workloads, particularly deep learning inference and training in its data centers.
  • D. Qualcomm AI Engine
    Qualcomm AI Engine is Qualcomm’s integrated hardware–software platform for accelerating on-device artificial intelligence tasks across its mobile and embedded chipsets.
  • E. EyeQ system-on-chip
    EyeQ system-on-chip is Mobileye’s specialized automotive processor platform designed to power advanced driver-assistance systems and autonomous driving functions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de90283b9c8190b50d30ad58bfe085 completed April 14, 2026, 7:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd551623608190ba1de09b423cc5e1 completed May 8, 2026, 3:14 a.m.
Created at: April 10, 2026, 1:16 a.m.